[Retracted] Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization

Table 3

Percent improvements of the AUCs of the mean RMSEs, CCs, and Oppo Costs compared with each original ALR approach.

		EGO-EMCM	EGO-QBC	EGO-GSx	RD-EGO
Ridge	RMSE	−0.96	−0.29	−1.07	1.31
	CC	−0.16	−0.27	−0.45	−0.13
	Oppo Cost	15.37	13.42	14.57	54.37
Lasso	RMSE	−0.99	−0.81	−0.82	3.48
	CC	1.20	−0.36	−0.48	−0.90
	Oppo Cost	22.17	10.92	16.35	55.45
Enet	RMSE	−0.70	−0.68	−0.75	−0.88
	CC	0.37	−0.55	−0.62	−1.99
	Oppo Cost	18.60	11.82	15.82	54.40